Everything Is Obvious_ _Once You Know the Answer - Duncan J. Watts [144]
9. See Orrell (2007) for a slightly different take on prediction in simple versus complex systems. See Gleick (1987), Watts (2003), and Mitchell (2009) for more general discussions of complex systems.
10. When I say we can predict only the probability of something happening, I am speaking somewhat loosely. The more correct way to talk about prediction for complex systems is that we ought to be able to predict properties of the distribution of outcomes, where this distribution characterizes the probability that a specified class of events will occur. So, for example, we might predict the probability that it will rain on a given day, or that the home team will win, or that a movie will generate more than a certain level of revenue. Equivalently, we might ask questions about the number of points by which we expect the home team to win, or the expected revenue of a particular class of movies to earn, or even the variance that we expect to observe around the average. Regardless, all these predictions are about “average properties” in the sense that they can be expressed as an expectation of some statistic over many draws from the distribution of outcomes.
11. For a die roll, it’s even worse: The best possible performance is to be right one time out of six, or less than 17 percent. In real life, therefore, where the range of possible outcomes can be much greater than a die roll—think, for example, of trying to predict the next bestseller—a track record of predicting the right outcome 20 percent of the time might very well be as good as possible. It’s just that being “right” 20 percent of the time also means being “wrong” 80 percent of the time; that just doesn’t sound very good.
12. See http://www.cimms.ou.edu/~doswell/probability/Probability.html. Orrell (2007) also presents an informative discussion of weather prediction; however, he is mostly concerned with longer-range forecasts, which are considerably less reliable.
13. Specifically, “frequentists” insist that statements about probabilities refer to the relative fraction of particular outcomes being realized, and therefore apply only to events, like flipping a coin, that can in principle be repeated ad infinitum. Conversely, the “evidential” view is that a probability should be interpreted only as the odds one ought to accept for a particular gamble, regardless of whether it is repeated or not.
14. See de Mesquita (2009) for details.
15. As Taleb explains, the term “black swan” derives from the European settlement of Australia: Until the settlers witnessed black swans in what is now Western Australia, conventional wisdom held that all swans must be white.
16. For details of the entire sequence of events surrounding the Bastille, see Sewell (1996, pp. 871–78). It is worth noting, moreover, that other historians of the French Revolution draw the boundaries rather differently from Sewell.
17. Taleb makes a similar point—namely that to have predicted the invention of what we now call the Internet, one would have to have known an awful lot about the applications to which the Internet was put after it had been invented. As Taleb puts it, “to understand the future to the point of being able to predict it, you need to incorporate elements from this future itself. If you know about the discovery you are about to make, then you have almost made it” (Taleb 2007, p. 172).
CHAPTER 7: THE BEST-LAID PLANS
1. Interestingly, a recent story in Time magazine (Kadlec 2010) contends that a new breed of poker players is relying on statistical